Recommendation algorithms play a pivotal role in shaping our media choices, which makes it crucial to comprehend their long-term impact on user behavior. These algorithms are often linked to two critical outcomes: homogenization, wherein users consume similar content despite disparate underlying preferences, and the filter bubble effect, wherein individuals with differing preferences only consume content aligned with their preferences (without much overlap with other users). Prior research assumes a trade-off between homogenization and filter bubble effects and then shows that personalized recommendations mitigate filter bubbles by fostering homogenization. However, because of this assumption of a tradeoff between these two effects, prior work cannot develop a more nuanced view of how recommendation systems may independently impact homogenization and filter bubble effects. We develop a more refined definition of homogenization and the filter bubble effect by decomposing them into two key metrics: how different the average consumption is between users (inter-user diversity) and how varied an individual's consumption is (intra-user diversity). We then use a novel agent-based simulation framework that enables a holistic view of the impact of recommendation systems on homogenization and filter bubble effects. Our simulations show that traditional recommendation algorithms (based on past behavior) mainly reduce filter bubbles by affecting inter-user diversity without significantly impacting intra-user diversity. Building on these findings, we introduce two new recommendation algorithms that take a more nuanced approach by accounting for both types of diversity.

翻译：推荐算法在塑造媒体选择方面发挥关键作用，因此理解其对用户行为的长期影响至关重要。此类算法通常关联两种关键结果：同质化（具有不同潜在偏好的用户消费相似内容）和过滤气泡效应（偏好不同的个体仅消费与其偏好一致的内容，与其他用户重叠较少）。以往研究假设同质化与过滤气泡效应之间存在权衡，进而表明个性化推荐通过促进同质化来缓解过滤气泡。然而，由于这种权衡假设，既往工作无法深入剖析推荐系统如何独立影响同质化与过滤气泡效应。我们通过将两者分解为两个核心指标——用户间平均消费差异（用户间多样性）与个体消费内容差异（用户内多样性）——对同质化与过滤气泡效应进行了更精确的定义。随后采用新颖的基于智能体的仿真框架，系统评估推荐系统对同质化与过滤气泡效应的影响。仿真结果表明，传统推荐算法（基于历史行为）主要通过调控用户间多样性来减弱过滤气泡，而对用户内多样性影响甚微。基于此发现，我们提出两种新型推荐算法，通过兼顾两类多样性实现更精细化的干预策略。